Once a distant goal, discovery science for human brain function is now a reality. Capitalizing on the ease of data-sharing with resting state fMRI (R-fMRI), the 1000 Functional Connectomes Project (FCP) has invigorated the neuroimaging community by aggregating datasets independently collected by labs around the world, and making them publicly available without restriction. As of Dec 11, 2009, researchers who once struggled to obtain 20 - 30 datasets for analyses suddenly had access to over 1200 datasets. Most importantly, feasibility analyses performed by the founding members of the FCP demonstrated the ability to carry out discovery science with the aggregate dataset. In order for discovery science to take hold in the neuroimaging field, researchers need continued access to large-scale imaging datasets that will enable both data mining and replication studies. To address this need, the FCP launched the International Neuroimaging Data-sharing Initiative (INDI). Designed to encourage the open sharing of more detailed phenotypic data with imaging datasets, and to establish a model for sharing data prospectively (i.e., prepublication), INDI is making significant strides in this regard. The present proposal seeks to extend this impact by overcoming a second key obstacle faced by prospective researchers - namely, scientists need access to appropriate tools to facilitate data exploration. This applies particularly to investigators who are inexperienced with the nuances of fMRI image analysis, or lack the programming support or resources necessary for handling and analyzing large- scale datasets. The aim of the proposed work is to create an open-source user interface for flexible, automated processing of R-fMRI datasets by both novice and expert users. An additional aim is to provide benchmark results so that users can calibrate their local results. Attaining these aims within the 24 months of the project will substantially accelerate the trajectory of discovery science of human brain function. !